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About This Role
Join the Team Modernizing Medicine
At ModMed, we’re not just building software—we’re reimagining the healthcare experience. Founded in 2010 by a practicing physician and a successful tech entrepreneur, we took a radically different approach: we hired doctors and taught them how to code. This "for doctors, by doctors" philosophy has allowed us to create an AI\-enabled, specialty\-specific cloud platform that places patients at the center of care.
A Culture of Excellence
When you join ModMed, you’re joining an award\-winning team recognized for innovation and employee satisfaction. From our global headquarters in Boca Raton Florida, and extensive employee base in Hyderabad India, we are a team of 4,500\+ passionate problem\-solvers on a mission to increase medical practice success and improve patient outcomes:
- Consistently ranked as a Top Place to Work
- 2025 Globee Business Awards: Gold Globee for “Technology Team of the Year”
- 2025 Black Book Awards: Ranked \#1 EHR in 11 Specialties
- Florida Venture Forum: Venture\-Backed Company of the Year
We are growing fast, thinking big, and we are just getting started.
Ready to modernize medicine with us?
Job Description Summary:
As a Staff AI Engineer, you define and drive the architecture of AI and agentic systems across multiple teams and product domains. This is a senior individual\-contributor leadership role: you influence high\-impact architectural decisions, evolve the practices and standards for building agentic AI, and turn experimental AI capabilities into reliable production systems. You set direction for multi\-agent orchestration, production RAG (hybrid search, re\-ranking, and query routing), tool and MCP integration, and the evaluation and observability stack that keeps them dependable. You mentor senior engineers and represent AI engineering in cross\-functional and strategic initiatives. A background in classical ML is an asset; the primary requirement is a proven track record of shipping production agentic AI.
KEY RESPONSIBILITIES
- Define and drive technical direction for AI and agentic systems, and contribute to the AI platform roadmap across teams
- Influence architecture decisions for compute, cloud, and AI infrastructure across teams
- Lead the design of large\-scale AI/LLM systems: inference platforms, APIs, and distributed architectures
- Architect production multi\-agent systems end\-to\-end: orchestration, state management, tool integration, and failure handling
- Define and drive best practices and standards for AI/LLM systems across teams (agent design, evaluation, observability, reliability)
- Lead complex production debugging and incident response across teams, and harden the resulting fixes into platform guardrails
- Mentor senior engineers and emerging technical leaders, raising the engineering bar
- Lead technical design reviews and architecture decision records (ADRs) for critical AI infrastructure
- Contribute to capacity planning and cost optimization strategies for AI/LLM infrastructure
GENAI / AGENTIC AI CAPABILITIES
- Define and drive vector database and RAG architecture decisions across systems and teams: structured RAG, hybrid search (dense \+ sparse \+ keyword), re\-ranking, and query routing
- Lead multi\-agent platform architecture decisions: runtime selection, orchestration patterns, and enterprise integration strategy
- Set the technical direction for MCP (Model Context Protocol) adoption and agent runtime infrastructure
- Shape agent infrastructure adoption: evaluate and standardize frameworks, tooling, and deployment patterns for agentic AI
- Architect evaluation infrastructure for non\-deterministic LLM systems: synthetic golden\-set generation, hierarchical weighted scoring (component, composite, and system\-level F1\), bootstrap confidence intervals, and paired A/B comparison, treating a change as real only when it is both statistically significant and clears a minimum effect size
- Gate deployments on eval results: tiered regression thresholds (hard\-gate vs monitor components) wired into CI so a measurable quality regression blocks the release, with observability via tracing across multi\-step chains and tool calls and drift detection on LLM inputs and outputs
- Drive LLM cost optimization at scale: model routing, caching, batching, token budget management, and provider cost analysis
REQUIRED SKILLS \& QUALIFICATIONS
- Master’s or Ph.D. degree in Computer Science, Software Engineering, or a related field.
- 10\+ years of professional experience in ML/AI or software engineering, including 4\+ years in senior or staff\-level roles with production system ownership
- Demonstrated engineering leadership, including driving technical strategy and influencing cross\-team decisions
- Expertise in platform and distributed\-systems architecture at scale: model serving, APIs, data platforms, and AI/LLM infrastructure
- Hands\-on experience architecting and operating production agentic AI or LLM systems (multi\-agent workflows, production RAG, tool and MCP integration)
- Deep understanding of embedding models, retrieval algorithms, and vector database internals
- Strong production debugging, reliability, and incident\-response skills
- Experience building rigorous evaluation for non\-deterministic AI systems, including statistical methods (such as bootstrap confidence intervals and minimum effect\-size thresholds) to separate genuine quality changes from run\-to\-run model variance
- Cost\-awareness for cloud AI/LLM workloads: capacity planning and cost optimization
- Proven mentorship of mid\-level and senior engineers
- Strong communication skills for executive and cross\-functional audiences
PREFERRED QUALIFICATIONS (NICE TO HAVE)
- Experience in Healthcare, FinTech, or other regulated industries
- Experience building AI/LLM systems or platform components from the ground up
- Defined best practices for AI\-assisted development (Claude Code): code quality standards, review, and responsible usage across teams
- Track record of conference talks, published papers, or significant open\-source contributions
- Experience with GPU\-accelerated inference and model serving optimization
- Familiarity with workflow orchestration and streaming architectures for real\-time AI
*ModMed Benefits Highlight:* At ModMed, we believe it’s important to offer a competitive benefits package designed to meet the diverse needs of our growing workforce. Eligible Modernizers can enroll in a wide range of benefits:
United States
- Comprehensive medical, dental, and vision benefits, including a company Health Savings Account contribution,
- 401(k): ModMed provides a matching contribution each payday of 50% of your contribution deferred on up to 6% of your compensation. After one year of employment with ModMed, 100% of any matching contribution you receive is yours to keep.
- Generous Paid Time Off and Paid Parental Leave programs,
- Company paid Life and Disability benefits, Flexible Spending Account, and Employee Assistance Programs,
- Company\-sponsored Business Resource \& Special Interest Groups that provide engaged and supportive communities within ModMed,
- Professional development opportunities, including tuition reimbursement programs and unlimited access to LinkedIn Learning,
- Global presence and in\-person collaboration opportunities; dog\-friendly HQ (US), Hybrid office\-based roles and remote availability for some roles,
- Weekly catered breakfast and lunch, treadmill workstations, Zen, and wellness rooms within our BRIC headquarters.
PHISHING SCAM WARNING: ModMed is among several companies recently made aware of a phishing scam involving imposters posing as hiring managers recruiting via email, text and social media. The imposters are creating misleading email accounts, conducting remote "interviews," and making fake job offers in order to collect personal and financial information from unsuspecting individuals. Please be aware that no job offers will be made from ModMed without a formal interview process, and valid communications from our hiring team will come from our employees with a ModMed email address ([email protected]). Please check senders’ email addresses carefully. Additionally, ModMed will not ask you to purchase equipment or supplies as part of your onboarding process. If you are receiving communications as described above, please report them to the FTC website.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Modernizing Medicine, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Modernizing Medicine, Inc. AI Hiring
Modernizing Medicine, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boca Raton, FL, US, Remote, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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